A Hybrid Control Strategy for Real-Time Control of the Iron Removal Process of the Zinc Hydrometallurgy Plants

Xie, Shiwen; Xie, Yongfang; Ying, Hao; Gui, Weihua; Yang, Chunhua

doi:10.1109/tii.2018.2815659

Cited by 33 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then the rates of the oxidation, hydrolysis, and neutralization reaction in the solution can be obtained from the chemical reaction kinetics: particles and s R is the radius of zinc oxide particles. Simulation based on the CSTR model of goethite process is proposed in reference [5]. In the research, the control variable available to the controller is oxygen.…”

Section: Description Of the Optimal Control Of Iron-removal Systemmentioning

confidence: 99%

“…It is a non-linear process involving a series of complex chemical reactions such as oxidation, hydrolysis, and neutralization. For the non-linear problems in the industrial production process, a lot of effective control methods have been proposed [3][4][5][6][7][8][9][10]. According to the law of mass conservation and reaction kinetics, Chen [4] et al established a single-tank continuous stirred reactor model based on the law of conservation of mass and reaction kinetics for the first time.…”

Section: Introductionmentioning

confidence: 99%

“…The distributed model predictive control strategy is adopted to solve the optimization control problem of iron-removal process. Xie [5] et al established a weighted coupled CSTR model for goethite iron-removal process by introducing weighting parameters. A parameter identification method to determine unknown parameters is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Description Of the Optimal Control Of Iron-removal Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, it can be seen that the proposed STA-APSNFIS model effectively reduces the complexity of the model. To achieve stable modeling results, 100 iterations were performed, and the final parameters are shown in Tab (2), the membership degrees of the four rules with the pre-parameter calculation and its fuzzy model are shown in Fig. 4…”

Section: Numerical Simulation and Model Robustness Verificationmentioning

confidence: 99%

“…Complex industrial processes (CIPs) [1] are often composed of closely-coupled sub-circuit processes, involving a series of strongly-coupled equipment. In the process production, many complex physico-chemical reactions occur in the equipment, usually acting in gas-liquid-solid threephase mixtures [2]. Specifically, CIPs involve a large amount of non-linear, time-varying parameters correlated with each other, but the detectable process monitoring information is often incomplete or lagged.…”

Section: Introductionmentioning

confidence: 99%

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Liu

Jiang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

show abstract

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Wang

Tang

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

show abstract

A Hybrid Control Strategy for Real-Time Control of the Iron Removal Process of the Zinc Hydrometallurgy Plants

Cited by 33 publications

References 22 publications

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Contact Info

Product

Resources

About